Image Improvement Through Metamaterial Technology for Brain Stroke Detection

Karadima, Olympia; Razzicchia, Eleonora; Kosmas, Panagiotis

doi:10.23919/eucap48036.2020.9135281

Cited by 4 publications

(6 citation statements)

References 17 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous study [ 19 ], we have already shown the benefits of using MTS-enhanced antennas. In this section, we present experimental results for both the arrays described in Section 2.2 .…”

Section: Resultsmentioning

confidence: 99%

“…To test the performance of our new PSMA and MTS-enhanced PSMA operating in air, we compared our previous developed brain scanner operating in an infinite lossy matching medium to two tomographic systems: an 8-PSMA system and an 8-MTS-enhanced PSMA system. Our previous developed MWT scanner for brain imaging consists of 12 spear patch antennas immersed in a 90% glycerol-water mixture and placed around EN 50361 Specific Anthropomorphic Mannequin (SAM) head model [ 18 , 19 ]. A similar setup (“System 1”), including 8 antennas placed elliptically around our numerical head model, was modelled in CST Microwave Studio.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…To couple the transmitted power into the body and improve the detection of useful scattered “weak” signals, we could take advantage of metasurface (MTS) technology. As demonstrated in our previous studies [ 18 , 19 ], one way to improve the efficacy of a MWT system is incorporating MTS structures into its array, adjacent to the substrate of each antenna. Using this hardware configuration, we have obtained higher quality reconstructed images of a blood-mimicking target placed inside the brain volume of our numerical head model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metasurface-Enhanced Antennas for Microwave Brain Imaging

Razzicchia

Guo

et al. 2021

Diagnostics

Self Cite

View full text Add to dashboard Cite

Stroke is a very frequent disorder and one of the major leading causes of death and disability worldwide. Timely detection of stroke is essential in order to select and perform the correct treatment strategy. Thus, the use of an efficient imaging method for an early diagnosis of this syndrome could result in an increased survival’s rate. Nowadays, microwave imaging (MWI) for brain stroke detection and classification has attracted growing interest due to its non-invasive and non-ionising properties. In this paper, we present a feasibility study with the goal of enhancing MWI for stroke detection using metasurface (MTS) loaded antennas. In particular, three MTS-enhanced antennas integrated in different brain scanners are presented. For the first two antennas, which operate in a coupling medium, we show experimental measurements on an elliptical brain-mimicking gel phantom including cylindrical targets representing the bleeding in haemorrhagic stroke (h-stroke) and the not oxygenated tissue in ischaemic stroke (i-stroke). The reconstructed images and transmission and reflection parameter plots show that the MTS loadings improve the performance of our imaging prototype. Specifically, the signal transmitted across our head model is indeed increased by several dB‘s over the desired frequency range of 0.5–2.0 GHz, and an improvement in the quality of the reconstructed images is shown when the MTS is incorporated in the system. We also present a detailed simulation study on the performance of a new printed square monopole antenna (PSMA) operating in air, enhanced by a MTS superstrate loading. In particular, our previous developed brain scanner operating in an infinite lossy matching medium is compared to two tomographic systems operating in air: an 8-PSMA system and an 8-MTS-enhanced PSMA system. Our results show that our MTS superstrate enhances the antennas’ return loss by around 5 dB and increases the signal difference due to the presence of a blood-mimicking target up to 25 dB, which leads to more accurate reconstructions. In conclusion, MTS structures may be a significant hardware advancement towards the development of functional and ergonomic MWI scanners for stroke detection.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metasurface-Enhanced Antennas for Microwave Brain Imaging

Razzicchia

Guo

et al. 2021

Diagnostics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Valeria Mariano's team at the Polytechnic University of Turin used a dataset generated by full-wave simulation to test the type and direction of stroke [15]. The group of King's College London, Strand, London, applied the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm to differentiate the hemorrhagic and S ischemic strokes [16], [17], [18]. In the latest study, the team from Czech Technical University used support vector machines (SVMs) to establish a comprehensive training set, which can classify hemorrhagic stroke and ischemic stroke [19].…”

Section: ) (Corresponding Author: Ming Yu)mentioning

confidence: 99%

“…[16], [17], [18] Discriminate between ischemic and hemorrhagic stroke and imaged. The effect of the presence or absence of metamaterials on the effectiveness was compared.…”

Section: Differential Approximations and Distortedmentioning

confidence: 99%

Classification and Location of Cerebral Hemorrhage Points Based on SEM and SSA-GA-BP Neural Network

Li,

Wang,

et al. 2024

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

In this paper, a method to fast classify (Intradural hemorrhage, epidural hemorrhage, and cerebral parenchymal hemorrhage) and locate the bleeding points by using the Singularity Expansion Method (SEM) and Backpropagation (BP) neural network optimized by genetic algorithm (GA) and Sparrow Search Algorithm (SSA) is proposed. In the simulation model, the bleeding spot with a radius of 3 mm is successfully identified by the approach. The test accuracy in the simulation for both the bleeding's localization and classification are 98.0% and 97.4%, respectively. Head phantoms that have all been improved over the previous phantom established are used for experiments. A bleeding target with a volume of 3 ml can be identified in the microwave detection system. In the experiment, the accuracy of classification and localization of the bleeding type are 90% and 94.7%, respectively. The final results demonstrate the capability and effectiveness of the method. Faster determination of bleeding point type and orientation means that patients can be provided with different rescue measures accordingly.

show abstract